A Method of Activity Recognition in Small-Scale Activity Classification Problems via Optimization of Deep Neural Networks


KIPS Transactions on Software and Data Engineering, Vol. 6, No. 3, pp. 155-160, Mar. 2017
10.3745/KTSDE.2017.6.3.155,   PDF Download:
Keywords: Activity recognition, Deep Neural Network, Optimization
Abstract

Recently, Deep learning has been used successfully to solve many recognition problems. It has many advantages over existing machine learning methods that extract feature points through hand-crafting. Deep neural networks for human activity recognition split video data into frame images, and then classify activities by analysing the connectivity of frame images according to the time. But it is difficult to apply to actual problems which has small-scale activity classes. Because this situations has a problem of overfitting and insufficient training data. In this paper, we defined 5 type of small-scale human activities, and classified them. We construct video database using 700 video clips, and obtained a classifying accuracy of 74.00%.


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Cite this article
[IEEE Style]
S. Kim, Y. Kim, D. Kim, "A Method of Activity Recognition in Small-Scale Activity Classification Problems via Optimization of Deep Neural Networks," KIPS Transactions on Software and Data Engineering, vol. 6, no. 3, pp. 155-160, 2017. DOI: 10.3745/KTSDE.2017.6.3.155.

[ACM Style]
Seunghyun Kim, Yeon-Ho Kim, and Do-Yeon Kim. 2017. A Method of Activity Recognition in Small-Scale Activity Classification Problems via Optimization of Deep Neural Networks. KIPS Transactions on Software and Data Engineering, 6, 3, (2017), 155-160. DOI: 10.3745/KTSDE.2017.6.3.155.